Investigation of a ternary blend of diesel/ethanol/n-butanol with binary nano additives on combustion and emission: A modeling and optimization approach with artificial neural networks

Abstract

The increase in global energy consumption and concerns about fossil fuels depletion are pushing the demand for renewable and clean energy sources. Alcohols are a promising candidate for internal combustion engines as alternative fuels. This study aims to reveal the effect of a ternary blend, which is denominated D80E10nB10, consisting of diesel (80%), ethanol (10%), and n-butanol (10%) on combustion and emission characteristics. The addition of both metal oxide (TiO2) and nonmetallic nanoparticle (MWCNT) was also investigated with the above-mentioned ternary blend fuels, which are denominated m- . The compression ignition engine was run under dual fuel mode with feeding H-2 in the range of 5 and 15 g/h, which were labeled D80E10nB10H5 and 15. The results revealed that the maximum pressure increased by 3.63 and 3.94% by adding 5 and 15 g/h H-2 respectively into ternary blend fuel under the full load. The highest BTE was obtained from modified diesel fuel which was 23.24% higher than diesel. Furthermore, 60 Artificial Neural Network (ANN) models were built to optimize test conditions. The optimal conditions for ternary blend fuel were found to be 4 g/h H-2 feeding rate while 5.5 g/h H-2 addition with modified ternary blend fuel would be the optimal.